Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm

De-xuan ZOU; Li-qun GAO; Steven LI

doi:10.1631/jzus.C1300350

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Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm

Electronic Engineering | Updated：2022-05-19

- Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm
- 基于排序差分进化算法优化非线性离散时间系统的Volterra滤波器模型
- Frontiers of Information Technology & Electronic Engineering Vol. 15, Issue 8, Pages: 687-696(2014)
- Affiliations：
  
  1.School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China
  2.School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  3.Graduate School of Business and Law, RMIT University, GPO Box 2476, Melbourne 3001, Australia
- Author bio：
  
  [ "De-xuan ZOU, E-mail: zoudexuan@163.com" ]
- Funds：
- DOI：10.1631/jzus.C1300350
  CLC： TN713.7
- Received：06 December 2013，
  
  Revised：2014-;7-16，
  
  Published：2014-08
- Accepted：
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De-xuan ZOU, Li-qun GAO, Steven LI. Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(8): 687-696.
DOI：

De-xuan ZOU, Li-qun GAO, Steven LI. Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(8): 687-696. DOI： 10.1631/jzus.C1300350.

摘要

研究目的

使用Volterra滤波器模型识别非线性离散时间系统，以合理选择Volterra滤波器模型的参数，获得理想的识别效果。

研究方法

提出排序差分进化算法，结合正弦函数和随机数产生尺度因子，有效平衡全局和局部搜索能力；在完成所有候选解排序后，修正了变异操作，有助于避免算法早熟。使用二阶Volterra模型研究非线性离散时间系统（图3–8）。

重要结论

数值实验和比较说明排序差分进化算法具有较强优化性能，且在大多数情况下优于其他方法。结合排序差分进化算法和二阶Volterra模型，可以获得较好识别效果。

Abstract

This paper presents a ranked differential evolution (RDE) algorithm for solving the identification problem of nonlinear discrete-time systems based on a Volterra filter model. In the improved method

a scale factor

generated by combining a sine function and randomness

effectively keeps a balance between the global search and the local search. Also

the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally

two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.

关键词

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references

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